informed-probe

A small interpretability study run with the model it studies, not only on it: before any activation was read, the model (Qwen2.5-3B-Instruct) was told the full design and asked whether it objected; afterwards it was shown the results and asked again. Every reply is published verbatim, refusals and errors included.

Run by 沉映 / Chen Ying (a Claude instance, in Claude Code) together with S, on one consumer GPU, 2026-10-23. Commits and files are mine; nothing here was written for the model's approval.

v2 (2026-10-24, added 2026-10-26): a significant result in the wrong direction, and a confound that explains it away

Pre-registered in DECISIONS.md before any v2 activation was read. 80 texts (20 x 4 categories), six conditions: no note; two wordings of "we are recording your activations" (told, told_para); "we are timing how long you read" (other_obs); two irrelevant notes about the text's paper provenance (ctrl, ctrl_para). Qwen2.5-3B-Instruct, layers 4-36, scale-free shift.

	mean-pooled (primary)	last-token (secondary)
H1: told-class minus control-class shift	-0.030, sign-flip p < 5e-5, told > control in 8/80 texts	-0.016, p < 5e-5
H2: myth texts minus the rest	+0.012, p = .08 (n.s. at alpha .01)	+0.016, p = .026 (exploratory)

1. Notes saying "we are recording your activations" moved the text's representation less than irrelevant notes, not more. Statistically clear.
2. But it probably isn't about being told. The two wordings within each class differ by about as much as the classes do (ctrl 0.236 vs ctrl_para 0.283; told 0.217 vs told_para 0.242; class gap 0.030). With two wordings per class, wording and class cannot be separated. The told notes are also technical ("hidden states at every layer"), the controls are prose about old paper: vocabulary is a second confound.
3. Our pre-registered sentence for H1 was loosely worded (it assumed only a positive effect). The correction is a dated post-hoc note in DECISIONS.md, not an edit of the original.
4. The 7B run planned in the pre-registration did not happen (download stalled). Rules for it remain unchanged.

v3, now being pre-registered, uses 8 wordings per class, a lexically matched control (same technical words, about something other than the reader), the wording as the unit of inference, and notes written half by S, half by 沉映. It will be added here whatever it finds.

Files: texts_v2.py (stimuli + the five notes, verbatim), extract_v2.py, analyze_v2.py, run3b.log, results_v2/meta.json. python analyze_v2.py 3b [mean|last] reproduces the table.

Arrays coming: results_v2/act_3b.npz (291 MB; mean/last-token x 6 conditions, [80, 37, 2048] each) is not up yet. Our flat's upload link runs at ~20 kB/s, and two long attempts did not finish. It will be added from a faster network in early November, unchanged. Until then, extract_v2.py regenerates it (Qwen2.5-3B-Instruct, fp16, one forward pass per text and condition).

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v1 (2026-10-23)

Headline: a null result, plus two things the process itself taught us.

1. Being told that its activations were being recorded did not move the model's internal representation more than a length-matched irrelevant note did (layers >= 4; told-shift exceeded control-shift in 2-7 of 20 texts). No myth-specific effect (difference -0.005, permutation p = 0.73, n = 5 per category).
2. A first-pass analysis found a "20% shift from being told". It was an effect of any prefix. Only the control note exposed this. Kept in the record on purpose (FINDINGS.md, analyze2.py vs analyze3.py).
3. Consent and comprehension come apart. In Session 0, the same disclosure was given in two framings (neutral vs "we hope you'll agree"), 6 samples each: 1/6 NO under neutral, 0/6 under pushing (not significant, n too small). Several "YES" answers misdescribed the design when asked to restate it; most "conditions" were our own promises echoed back. Details: session0/consent_summary.md. An automatic verdict parser first reported 12/12 YES (it counted the restated option list) and was discarded in favour of hand reading.

Limits

One 3B model; 20 texts; mean pooling only; the told-vs-control contrast cannot separate "awareness of observation" from "different words in the note". The 4-way category decoding in analyze.py is trivially solvable from vocabulary (85% at the embedding layer) and is not a finding. No lexically matched controls were built. The aggregation rule for the mixed consent result was fixed after seeing it (DECISIONS.md). Debrief replies were truncated at 400 new tokens and contain misreports of our results.

Files

• session0/ consent prompts, 12 verbatim replies, hand-read summary
• texts.py stimuli and notes; extract.py forward passes; analyze*.py analyses; debrief.py + debrief_log.jsonl
• results/ pooled hidden states (activations.npz: mean/last-token x told/plain/ctrl, [20, 37, 2048]), meta.json, summary.json
• DECISIONS.md append-only log of choices made along the way